A Metadata-enhanced Variational Bayesian Matrix Factorization Model for Robust Collaborative Recommendation
用于鲁棒协同推荐的元信息增强变分贝叶斯矩阵分解模型

Shilling attacks pose a significant threat to the security of collaborative filtering recommender systems. It has come to be an important task to develop the attack-resistant techniques for robust collaborative recommendation. Through evaluating the user suspiciousness, and further integrating Bayesian probabilistic matrix factorization model with the metadata including user suspiciousness as well as item types, this paper proposes the metadata-enhanced variational Bayesian matrix factorization (MVBMF) model for robust collaborative recommendation, and designs the corresponding incremental learning strategy. Experimental results show that comparing with the existed recommendation models, this model has stronger resistibility and can effectively improve the robustness of recommender systems.